{ "metadata": { "version": "1.2.0", "updated_at": "2026-10-19", "search_refreshed_at": "2026-10-19", "topic_id": "generated-adapters", "name": "Generated weights and adapters", "definition": "Generate model weights from a task description, examples, or context.", "record_count": 7, "topic_url": "https://programasweights.com/reading-list/guides/compilation/#generated-adapters", "source_url": "https://programasweights.com/reading-list/data/papers.json" }, "comparisons": [ { "title": "From generated adapters to reusable functions", "text": "Hypter, HINT, HyperTuning, and Text-to-LoRA generate adaptations from descriptions or examples. PAW uses generated parameters as part of a program that can be saved, called from ordinary code, and reused by a fixed interpreter.", "source_ids": [ "2101.00420", "2212.10315", "2211.12485", "2506.06105", "2607.02512" ], "source_urls": [ "https://arxiv.org/html/2607.02512v1#S10", "https://proceedings.mlr.press/v267/charakorn25a.html" ], "source_labels": [ "PAW related work", "Text-to-LoRA paper" ] } ], "demos": [ ], "papers": [ { "id": "1609.09106", "title": "HyperNetworks", "authors": "David Ha, Andrew Dai, Quoc V. Le", "first_public": "2016-09-27", "first_public_source": "https://arxiv.org/abs/1609.09106", "arxiv_first_submission": "2016-09-27", "date_label": "arXiv v1", "arxiv": "1609.09106", "paper_url": "https://arxiv.org/abs/1609.09106", "venue": "ICLR 2017", "lens": [ "generated-adapters" ], "related_terms": [ "hypernetwork", "weight generation", "fast weights", "conditional parameters" ], "summary": "Uses one neural network to generate the weights of another.", "contribution": "Applies weight generation to convolutional and recurrent networks, relaxing weight sharing across layers and generating dynamic weights for sequence models.", "limitations": "The hypernetwork is trained end to end for the target architecture.", "program_form": "Weights for the target network.", "conditioning": "Layer embeddings or the current recurrent state.", "execution": "The target network runs with the generated weights.", "relationship": "Provides the weight-generation mechanism used by later description-to-adapter methods and PAW.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "David Ha", "Andrew Dai", "Quoc V. Le" ], "record_url": "https://programasweights.com/reading-list/papers/1609.09106/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-1609-09106", "citation_date": "2016-09-27", "json_url": "https://programasweights.com/reading-list/papers/1609.09106/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/1609.09106/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/1609.09106/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/1609.09106/citation.csl.json", "publication": { "title": "HyperNetworks", "author_names": [ "David Ha", "Andrew M. Dai", "Quoc V. Le" ], "year": 2017, "type": "paper-conference", "container_title": "International Conference on Learning Representations", "url": "https://openreview.net/forum?id=rkpACe1lx", "source_url": "https://openreview.net/pdf?id=rkpACe1lx", "verified_at": "2026-10-19" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/1609.09106/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/1609.09106/publication.csl.json" }, { "id": "2101.00420", "title": "Learning to Generate Task-Specific Adapters from Task Description", "authors": "Qinyuan Ye, Xiang Ren", "first_public": "2021-01-02", "first_public_source": "https://arxiv.org/abs/2101.00420", "arxiv_first_submission": "2021-01-02", "date_label": "arXiv v1", "arxiv": "2101.00420", "paper_url": "https://arxiv.org/abs/2101.00420", "venue": "ACL-IJCNLP 2021", "lens": [ "generated-adapters" ], "related_terms": [ "Hypter", "task descriptions", "generated adapters", "zero-shot learning" ], "summary": "Introduces Hypter, which generates adapters from natural-language task descriptions.", "contribution": "Combines a description encoder and adapter generator with BART. Tests generalization to unseen tasks on ZEST and synthetic SQuAD.", "limitations": "Training requires a collection of tasks with descriptions and labeled examples.", "program_form": "Bottleneck adapters inserted into a text-to-text model.", "conditioning": "A natural-language task description.", "execution": "BART performs the task with the generated adapters.", "relationship": "An early description-to-adapter method cited by PAW. PAW develops the programming interface for saving and calling the resulting neural functions.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Qinyuan Ye", "Xiang Ren" ], "record_url": "https://programasweights.com/reading-list/papers/2101.00420/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2101-00420", "citation_date": "2021-01-02", "json_url": "https://programasweights.com/reading-list/papers/2101.00420/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2101.00420/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2101.00420/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2101.00420/citation.csl.json", "publication": { "title": "Learning to Generate Task-Specific Adapters from Task Description", "author_names": [ "Qinyuan Ye", "Xiang Ren" ], "year": 2021, "type": "paper-conference", "container_title": "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)", "url": "https://aclanthology.org/2021.acl-short.82/", "source_url": "https://aclanthology.org/2021.acl-short.82/", "verified_at": "2026-10-19", "doi": "10.18653/v1/2021.acl-short.82", "publisher": "Association for Computational Linguistics", "pages": "646-653" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2101.00420/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2101.00420/publication.csl.json", "method_comparisons": [ { "title": "From generated adapters to reusable functions", "text": "Hypter, HINT, HyperTuning, and Text-to-LoRA generate adaptations from descriptions or examples. PAW uses generated parameters as part of a program that can be saved, called from ordinary code, and reused by a fixed interpreter.", "source_ids": [ "2101.00420", "2212.10315", "2211.12485", "2506.06105", "2607.02512" ], "source_urls": [ "https://arxiv.org/html/2607.02512v1#S10", "https://proceedings.mlr.press/v267/charakorn25a.html" ], "source_labels": [ "PAW related work", "Text-to-LoRA paper" ], "topic_id": "generated-adapters", "topic_label": "Generated weights and adapters", "topic_url": "https://programasweights.com/reading-list/guides/compilation/#generated-adapters" } ] }, { "id": "2211.12485", "title": "HyperTuning: Toward Adapting Large Language Models without Back-propagation", "authors": "Jason Phang, Yi Mao, Pengcheng He, Weizhu Chen", "first_public": "2022-11-22", "first_public_source": "https://arxiv.org/abs/2211.12485", "arxiv_first_submission": "2022-11-22", "date_label": "arXiv v1", "arxiv": "2211.12485", "paper_url": "https://arxiv.org/abs/2211.12485", "venue": "ICML 2023", "lens": [ "generated-adapters" ], "related_terms": [ "HyperTuning", "HyperT5", "few-shot adaptation", "generated LoRA" ], "summary": "Produces task-specific parameters for a frozen language model from a few demonstrations.", "contribution": "Introduces HyperT5 models that generate prefixes or LoRA parameters. Hyperpretraining and multitask training prepare the generator for new tasks.", "limitations": "The hypermodel accepts a limited number of demonstrations. The paper also studies further task-specific tuning.", "program_form": "Generated prefix or LoRA parameters.", "conditioning": "A few examples of the task.", "execution": "A frozen T5 model runs with the generated parameters.", "relationship": "PAW cites HyperTuning as prior work on generating adaptation parameters from examples.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Jason Phang", "Yi Mao", "Pengcheng He", "Weizhu Chen" ], "record_url": "https://programasweights.com/reading-list/papers/2211.12485/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2211-12485", "citation_date": "2022-11-22", "json_url": "https://programasweights.com/reading-list/papers/2211.12485/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2211.12485/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2211.12485/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2211.12485/citation.csl.json", "publication": { "title": "HyperTuning: Toward Adapting Large Language Models without Back-propagation", "author_names": [ "Jason Phang", "Yi Mao", "Pengcheng He", "Weizhu Chen" ], "year": 2023, "type": "paper-conference", "container_title": "Proceedings of the 40th International Conference on Machine Learning", "url": "https://proceedings.mlr.press/v202/phang23a.html", "source_url": "https://proceedings.mlr.press/v202/phang23a.html", "verified_at": "2026-10-19", "volume": "202", "publisher": "PMLR", "pages": "27854-27875" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2211.12485/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2211.12485/publication.csl.json", "method_comparisons": [ { "title": "From generated adapters to reusable functions", "text": "Hypter, HINT, HyperTuning, and Text-to-LoRA generate adaptations from descriptions or examples. PAW uses generated parameters as part of a program that can be saved, called from ordinary code, and reused by a fixed interpreter.", "source_ids": [ "2101.00420", "2212.10315", "2211.12485", "2506.06105", "2607.02512" ], "source_urls": [ "https://arxiv.org/html/2607.02512v1#S10", "https://proceedings.mlr.press/v267/charakorn25a.html" ], "source_labels": [ "PAW related work", "Text-to-LoRA paper" ], "topic_id": "generated-adapters", "topic_label": "Generated weights and adapters", "topic_url": "https://programasweights.com/reading-list/guides/compilation/#generated-adapters" } ] }, { "id": "2212.10315", "title": "HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation", "authors": "Hamish Ivison, Akshita Bhagia, Yizhong Wang, Hannaneh Hajishirzi, Matthew Peters", "first_public": "2022-12-20", "first_public_source": "https://arxiv.org/abs/2212.10315", "arxiv_first_submission": "2022-12-20", "date_label": "arXiv v1", "arxiv": "2212.10315", "paper_url": "https://arxiv.org/abs/2212.10315", "venue": "ACL 2023", "lens": [ "generated-adapters" ], "related_terms": [ "HINT", "instruction tuning", "hypernetwork", "instruction fusion" ], "summary": "Turns instructions and examples into reusable prefixes, adapters, and encoded instruction representations.", "contribution": "Combines a hypernetwork with instruction fusion so repeated calls can reuse the instruction computation. Evaluates zero-shot and few-shot generalization.", "limitations": "The generated modules and cached instruction representations are tied to the trained encoder-decoder model.", "program_form": "Prefixes, adapters, and encoded instruction representations.", "conditioning": "Task instructions and optional examples.", "execution": "An encoder-decoder model uses the generated modules and attends to cached instruction representations.", "relationship": "HINT reuses generated modules and an encoded instruction. PAW reuses a program containing text and adaptation parameters.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Hamish Ivison", "Akshita Bhagia", "Yizhong Wang", "Hannaneh Hajishirzi", "Matthew Peters" ], "record_url": "https://programasweights.com/reading-list/papers/2212.10315/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2212-10315", "citation_date": "2022-12-20", "json_url": "https://programasweights.com/reading-list/papers/2212.10315/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2212.10315/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2212.10315/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2212.10315/citation.csl.json", "publication": { "title": "HINT: Hypernetwork Instruction Tuning for Efficient Zero- and Few-Shot Generalisation", "author_names": [ "Hamish Ivison", "Akshita Bhagia", "Yizhong Wang", "Hannaneh Hajishirzi", "Matthew E. Peters" ], "year": 2023, "type": "paper-conference", "container_title": "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", "url": "https://aclanthology.org/2023.acl-long.631/", "source_url": "https://aclanthology.org/2023.acl-long.631/", "verified_at": "2026-10-19", "doi": "10.18653/v1/2023.acl-long.631", "publisher": "Association for Computational Linguistics", "pages": "11272-11288" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2212.10315/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2212.10315/publication.csl.json", "method_comparisons": [ { "title": "From generated adapters to reusable functions", "text": "Hypter, HINT, HyperTuning, and Text-to-LoRA generate adaptations from descriptions or examples. PAW uses generated parameters as part of a program that can be saved, called from ordinary code, and reused by a fixed interpreter.", "source_ids": [ "2101.00420", "2212.10315", "2211.12485", "2506.06105", "2607.02512" ], "source_urls": [ "https://arxiv.org/html/2607.02512v1#S10", "https://proceedings.mlr.press/v267/charakorn25a.html" ], "source_labels": [ "PAW related work", "Text-to-LoRA paper" ], "topic_id": "generated-adapters", "topic_label": "Generated weights and adapters", "topic_url": "https://programasweights.com/reading-list/guides/compilation/#generated-adapters" } ] }, { "id": "2411.05877", "title": "Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass", "authors": "Tong Chen, Hao Fang, Patrick Xia, Xiaodong Liu, Benjamin Van Durme, Luke Zettlemoyer, Jianfeng Gao, Hao Cheng", "first_public": "2024-11-08", "first_public_source": "https://arxiv.org/abs/2411.05877", "arxiv_first_submission": "2024-11-08", "date_label": "arXiv v1", "arxiv": "2411.05877", "paper_url": "https://arxiv.org/abs/2411.05877", "venue": "ICLR 2025", "lens": [ "generated-adapters", "context-compression" ], "related_terms": [ "Generative Adapter", "contextualization", "fast weights", "streaming context" ], "summary": "Converts new context into low-rank model updates in one forward pass.", "contribution": "Trains an adapter generator with self-supervision. Tests learning from documents, demonstrations, and conversation history.", "limitations": "The adapter generator is tied to its frozen base model.", "program_form": "Additive low-rank adapters.", "conditioning": "Documents, demonstrations, or conversation history, including successive chunks of context.", "execution": "The adapted model answers new inputs using context stored in its parameter updates.", "relationship": "PAW cites this context-to-adapter method. Generative Adapter stores information from context; PAW compiles the behavior of a function.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "first_public_note": "An AFM 2024 OpenReview version appeared before the first arXiv submission.", "author_names": [ "Tong Chen", "Hao Fang", "Patrick Xia", "Xiaodong Liu", "Benjamin Van Durme", "Luke Zettlemoyer", "Jianfeng Gao", "Hao Cheng" ], "record_url": "https://programasweights.com/reading-list/papers/2411.05877/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2411-05877", "citation_date": "2024-11-08", "json_url": "https://programasweights.com/reading-list/papers/2411.05877/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2411.05877/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2411.05877/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2411.05877/citation.csl.json", "publication": { "title": "Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass", "author_names": [ "Tong Chen", "Hao Fang", "Patrick Xia", "Xiaodong Liu", "Benjamin Van Durme", "Luke Zettlemoyer", "Jianfeng Gao", "Hao Cheng" ], "year": 2025, "type": "paper-conference", "container_title": "International Conference on Learning Representations", "url": "https://openreview.net/forum?id=bc3sUsS6ck", "source_url": "https://openreview.net/pdf?id=bc3sUsS6ck", "verified_at": "2026-10-19" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2411.05877/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2411.05877/publication.csl.json", "method_comparisons": [ { "title": "What gets reused?", "text": "Gisting caches a few instruction activations. Generative Adapter stores context in parameter updates. PAW saves a function as a program containing text and a neural module.", "source_ids": [ "2304.08467", "2411.05877", "2607.02512" ], "source_urls": [ "https://arxiv.org/abs/2304.08467", "https://arxiv.org/abs/2411.05877", "https://arxiv.org/html/2607.02512v1#S2" ], "source_labels": [ "Gist paper", "Generative Adapter paper", "PAW program definition" ], "topic_id": "context-compression", "topic_label": "Context compression", "topic_url": "https://programasweights.com/reading-list/guides/compilation/#context-compression" } ] }, { "id": "2506.06105", "title": "Text-to-LoRA: Instant Transformer Adaption", "authors": "Rujikorn Charakorn, Edoardo Cetin, Yujin Tang, Robert Tjarko Lange", "first_public": "2025-06-06", "first_public_source": "https://arxiv.org/abs/2506.06105", "arxiv_first_submission": "2025-06-06", "date_label": "arXiv v1", "arxiv": "2506.06105", "paper_url": "https://arxiv.org/abs/2506.06105", "venue": "ICML 2025", "lens": [ "generated-adapters" ], "related_terms": [ "Text-to-LoRA", "T2L", "task description", "adapter generation" ], "summary": "Uses a hypernetwork to generate LoRA adapters from task descriptions.", "contribution": "Trains hypernetworks to reconstruct existing adapters or learn from supervised task data. Tests adapter compression and generation for unseen tasks.", "limitations": "Reconstruction-trained T2L underperforms the supervised variant on unseen tasks.", "program_form": "Generated LoRA matrices.", "conditioning": "A natural-language task description.", "execution": "A base transformer performs the task with the generated LoRA.", "relationship": "PAW cites Text-to-LoRA as prior work on generating adapters. PAW develops fuzzy-function programming, FuzzyBench, and an interface for reusable neural programs.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Rujikorn Charakorn", "Edoardo Cetin", "Yujin Tang", "Robert Tjarko Lange" ], "record_url": "https://programasweights.com/reading-list/papers/2506.06105/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2506-06105", "citation_date": "2025-06-06", "json_url": "https://programasweights.com/reading-list/papers/2506.06105/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2506.06105/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2506.06105/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2506.06105/citation.csl.json", "publication": { "title": "Text-to-LoRA: Instant Transformer Adaption", "author_names": [ "Rujikorn Charakorn", "Edoardo Cetin", "Yujin Tang", "Robert Tjarko Lange" ], "year": 2025, "type": "paper-conference", "container_title": "Proceedings of the 42nd International Conference on Machine Learning", "url": "https://proceedings.mlr.press/v267/charakorn25a.html", "source_url": "https://proceedings.mlr.press/v267/charakorn25a.html", "verified_at": "2026-10-19", "volume": "267", "publisher": "PMLR", "pages": "7485-7514" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2506.06105/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2506.06105/publication.csl.json", "method_comparisons": [ { "title": "From generated adapters to reusable functions", "text": "Hypter, HINT, HyperTuning, and Text-to-LoRA generate adaptations from descriptions or examples. PAW uses generated parameters as part of a program that can be saved, called from ordinary code, and reused by a fixed interpreter.", "source_ids": [ "2101.00420", "2212.10315", "2211.12485", "2506.06105", "2607.02512" ], "source_urls": [ "https://arxiv.org/html/2607.02512v1#S10", "https://proceedings.mlr.press/v267/charakorn25a.html" ], "source_labels": [ "PAW related work", "Text-to-LoRA paper" ], "topic_id": "generated-adapters", "topic_label": "Generated weights and adapters", "topic_url": "https://programasweights.com/reading-list/guides/compilation/#generated-adapters" } ] }, { "id": "2607.02512", "title": "Program-as-Weights: A Programming Paradigm for Fuzzy Functions", "authors": "Wentao Zhang, Liliana Hotsko, Woojeong Kim, Pengyu Nie, Stuart Shieber, Yuntian Deng", "first_public": "2026-07-02", "first_public_source": "https://arxiv.org/abs/2607.02512", "arxiv_first_submission": "2026-07-02", "date_label": "arXiv v1", "arxiv": "2607.02512", "paper_url": "https://arxiv.org/abs/2607.02512", "venue": "NeurIPS 2026 (accepted)", "lens": [ "neural-programs", "generated-adapters" ], "related_terms": [ "Program-as-Weights", "ProgramAsWeights", "PAW", "fuzzy-function programming", "neural compiler", "neural interpreter", "FuzzyBench" ], "summary": "Proposes fuzzy-function programming: compile a natural-language function specification into a neural program, then reuse it locally.", "contribution": "Develops the compiler–interpreter abstraction and programs that combine text with learned parameters. Introduces the 10M-example FuzzyBench dataset, local execution interfaces, and image-conditioned compilation. Prefix tuning and LoRA are two implementations.", "limitations": "Each compiler produces programs for a specific interpreter. Compiled parameters are opaque, and FuzzyBench uses synthetic data.", "program_form": "A discrete pseudo-program and a compiler-generated parameter-efficient module.", "conditioning": "A function specification, optionally with examples. The image extension also accepts visual information.", "execution": "A frozen lightweight interpreter loads the program and runs new inputs locally.", "relationship": "Compile by Training builds on this programming abstraction. It keeps the same interface and refines the fast compiler’s prediction using synthesized examples.", "relationship_source": "https://arxiv.org/html/2609.04199v1#S1", "project_url": "https://programasweights.com/", "code_url": "https://github.com/programasweights", "equal_contribution": [ "Wentao Zhang", "Liliana Hotsko", "Woojeong Kim" ], "venue_source": "https://yuntiandeng.com/", "provenance": "July 2026 arXiv paper. NeurIPS 2026 acceptance is listed on Yuntian Deng’s homepage.", "author_names": [ "Wentao Zhang", "Liliana Hotsko", "Woojeong Kim", "Pengyu Nie", "Stuart Shieber", "Yuntian Deng" ], "record_url": "https://programasweights.com/reading-list/papers/2607.02512/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2607-02512", "citation_date": "2026-07-02", "json_url": "https://programasweights.com/reading-list/papers/2607.02512/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2607.02512/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2607.02512/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2607.02512/citation.csl.json", "method_comparisons": [ { "title": "From generated adapters to reusable functions", "text": "Hypter, HINT, HyperTuning, and Text-to-LoRA generate adaptations from descriptions or examples. PAW uses generated parameters as part of a program that can be saved, called from ordinary code, and reused by a fixed interpreter.", "source_ids": [ "2101.00420", "2212.10315", "2211.12485", "2506.06105", "2607.02512" ], "source_urls": [ "https://arxiv.org/html/2607.02512v1#S10", "https://proceedings.mlr.press/v267/charakorn25a.html" ], "source_labels": [ "PAW related work", "Text-to-LoRA paper" ], "topic_id": "generated-adapters", "topic_label": "Generated weights and adapters", "topic_url": "https://programasweights.com/reading-list/guides/compilation/#generated-adapters" }, { "title": "Code and neural programs", "text": "ALCHEmist generates symbolic labeling programs. PAW compiles fuzzy functions into text and learned parameters. Both build an artifact once and reuse it; one runs as code, the other on a neural interpreter.", "source_ids": [ "2407.11004", "2607.02512" ], "source_urls": [ "https://proceedings.neurips.cc/paper_files/paper/2024/hash/72802bef5cf1a3449e909b20c2ae18d5-Abstract-Conference.html", "https://arxiv.org/html/2607.02512v1#S6" ], "source_labels": [ "ALCHEmist paper", "PAW evaluation" ], "topic_id": "neural-programs", "topic_label": "Programs and reusable artifacts", "topic_url": "https://programasweights.com/reading-list/guides/fuzzy-functions/" }, { "title": "What gets reused?", "text": "Gisting caches a few instruction activations. Generative Adapter stores context in parameter updates. PAW saves a function as a program containing text and a neural module.", "source_ids": [ "2304.08467", "2411.05877", "2607.02512" ], "source_urls": [ "https://arxiv.org/abs/2304.08467", "https://arxiv.org/abs/2411.05877", "https://arxiv.org/html/2607.02512v1#S2" ], "source_labels": [ "Gist paper", "Generative Adapter paper", "PAW program definition" ], "topic_id": "context-compression", "topic_label": "Context compression", "topic_url": "https://programasweights.com/reading-list/guides/compilation/#context-compression" }, { "title": "Program form and compiler", "text": "LoRA represents adaptation as low-rank weight updates; prefix tuning uses continuous activations. Their original methods learn these parameters from examples. PAW generates both forms from specifications.", "source_ids": [ "2106.09685", "2101.00190", "2607.02512" ], "source_urls": [ "https://arxiv.org/abs/2106.09685", "https://aclanthology.org/2021.acl-long.353/", "https://arxiv.org/html/2607.02512v1#S3" ], "source_labels": [ "LoRA paper", "Prefix-tuning paper", "PAW implementations" ], "topic_id": "peft", "topic_label": "Parameter-efficient building blocks", "topic_url": "https://programasweights.com/reading-list/guides/compilation/#peft" }, { "title": "Fast prediction and training", "text": "PAW’s fast compiler predicts a program. Compile by Training starts from that prediction, generates examples, and trains an adapter. Both use the same runtime interface; the extra training improves semantic accuracy on FuzzyBench-Hard at a higher compilation cost.", "source_ids": [ "2607.02512", "2609.04199" ], "source_urls": [ "https://arxiv.org/html/2607.02512v1#S3", "https://arxiv.org/html/2609.04199v1#S3", "https://arxiv.org/html/2609.04199v1#S5" ], "source_labels": [ "PAW compiler implementations", "Compile by Training method", "Compile by Training evaluation" ], "topic_id": "training-compilation", "topic_label": "Compilation through training", "topic_url": "https://programasweights.com/reading-list/guides/compilation/#training-compilation" } ] } ] }